Resource partitioning for uplink in a wireless communication network

ABSTRACT

Techniques for performing resource partitioning are described. In an aspect, adaptive resource partitioning may be performed to dynamically allocate available resources for the uplink to nodes, e.g., base stations. Each node may be assigned a list of target interference-over-thermal (IoT) levels for the available resources by the adaptive resource partitioning. Each node may obtain a list of target IoT levels for itself and at least one list of target IoT levels for at least one neighbor node. The list of target IoT levels for each node may include a configurable target IoT level on each available resource for the node. Each node may schedule its UEs for transmission on the available resources (e.g., may determine transmit power levels and rates for the UEs) based on the target IoT levels for itself and the neighbor node(s) such that the target IoT levels for the neighbor node(s) can be met.

The present application is a Divisional application of U.S. Ser. No.12/725,137, filed Mar. 16, 2010, entitled “RESOURCE PARTITIONING FORUPLINK IN A WIRELESS COMMUNICATION NETWORK, which claims prioritypriority to provisional U.S. Application Ser. No. 61/161,652, entitled“ASSOCIATION AND RESOURCE COORDINATING FOR UPLINK IN HETEROGENEOUSNETWORKS,” filed Mar. 19, 2009, assigned to the assignee hereof andincorporated herein by reference.

BACKGROUND

I. Field

The present disclosure relates generally to communication, and morespecifically to techniques for supporting wireless communication.

II. Background

Wireless communication networks are widely deployed to provide variouscommunication content such as voice, video, packet data, messaging,broadcast, etc. These wireless networks may be multiple-access networkscapable of supporting multiple users by sharing the available networkresources. Examples of such multiple-access networks include CodeDivision Multiple Access (CDMA) networks, Time Division Multiple Access(TDMA) networks, Frequency Division Multiple Access (FDMA) networks,Orthogonal FDMA (OFDMA) networks, and Single-Carrier FDMA (SC-FDMA)networks.

A wireless communication network may include a number of base stationsthat can support communication for a number of user equipments (UEs). AUE may communicate with a base station via the downlink and uplink. Thedownlink (or forward link) refers to the communication link from thebase station to the UE, and the uplink (or reverse link) refers to thecommunication link from the UE to the base station.

A base station may serve one or more UEs at any given moment. On theuplink, a transmission from a UE may observe interference due totransmissions from other UEs communicating with neighbor base stations.The transmission from the UE may also cause interference to thetransmissions from the other UEs. The interference may degrade theperformance of all affected UEs. It may be desirable to mitigateinterference in order to improve performance.

SUMMARY

Techniques for performing resource partitioning for the uplink toimprove performance are described herein. Resource partitioning refersto a process to allocate available resources to nodes. A node may be abase station, a relay station, or some other entity. In an aspect,available resources for the uplink may be dynamically allocated to nodesby assigning each node with a list of target interference-over-thermal(IoT) levels for the available resources. Each node may schedule its UEsfor transmission on the available resources such that the target IoTlevels for neighbor nodes can be met.

In one design, a serving node for at least one UE may obtain a list oftarget IoT levels for itself and at least one list of target IoT levelsfor at least one neighbor node. The list of target IoT levels for eachnode may include a configurable target IoT level on each availableresource for that node. The serving node may schedule the at least oneUE for transmission on the available resources based on the lists oftarget IoT levels for the serving and neighbor nodes.

In one design of adaptive resource partitioning, the serving node maycompute local metrics for a plurality of possible actions related toallocation of the available resources to the serving and neighbor nodes.Each possible action may be associated with specific lists of target IoTlevels for the serving and neighbor nodes. The serving node may send thecomputed local metrics to the neighbor node(s) and may receive localmetrics for different possible actions from the neighbor node(s). Theserving node may determine overall metrics for the possible actionsbased on the computed local metrics and the received local metrics. Theserving node may select one possible action based on the overall metricsand may determine a list of target IoT levels for each node based on thelists of target IoT levels associated with the selected action.

In one design of scheduling, the serving node may determine targetinterference levels for the neighbor node(s) on the available resourcesbased on the target IoT levels for the neighbor node(s). The servingnode may determine transmit power spectral density (PSD) levels for theUE(s) on the available resources based on the target interference levelsfor the neighbor node(s). The serving node may also determine rates forthe UE(s) on the available resources based on the transmit PSD levelsfor the UE(s) and the target IoT levels for the serving node. Theserving node may assign the available resources to the UE(s). Theserving node may also determine an overall rate for each UE based on atleast one resource assigned to the UE and the rate achieved by the UE oneach assigned resource.

Various aspects and features of the disclosure are described in furtherdetail below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a wireless communication network.

FIG. 2 shows exemplary active sets for UEs and neighbor sets for nodes.

FIG. 3 shows a process for performing resource partitioning for theuplink.

FIG. 4 shows a process for supporting communication by a node.

FIG. 5 shows an apparatus for supporting communication by a node.

FIG. 6 shows a process for performing resource partitioning by a node.

FIG. 7 shows a process for scheduling UEs.

FIG. 8 shows a process for communicating by a UE.

FIG. 9 shows an apparatus for communicating by a UE.

FIG. 10 shows a block diagram of a base station and a UE.

DETAILED DESCRIPTION

The techniques described herein may be used for various wirelesscommunication networks such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA andother networks. The terms “network” and “system” are often usedinterchangeably. A CDMA network may implement a radio technology such asUniversal Terrestrial Radio Access (UTRA), cdma2000, etc. UTRA includesWideband CDMA (WCDMA) and other variants of CDMA. cdma2000 coversIS-2000, IS-95 and IS-856 standards. A TDMA network may implement aradio technology such as Global System for Mobile Communications (GSM).An OFDMA network may implement a radio technology such as Evolved UTRA(E-UTRA), Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16(WiMAX), IEEE 802.20, Flash-OFDM®, etc. UTRA and E-UTRA are part ofUniversal Mobile Telecommunication System (UMTS). 3GPP Long TermEvolution (LTE) and LTE-Advanced (LTE-A) are new releases of UMTS thatuse E-UTRA, which employs OFDMA on the downlink and SC-FDMA on theuplink. UTRA, E-UTRA, UMTS, LTE, LTE-A and GSM are described indocuments from an organization named “3rd Generation PartnershipProject” (3GPP). cdma2000 and UMB are described in documents from anorganization named “3rd Generation Partnership Project 2” (3GPP2). Thetechniques described herein may be used for the wireless networks andradio technologies mentioned above as well as other wireless networksand radio technologies.

FIG. 1 shows a wireless communication network 100, which may include anumber of base stations 110 and other network entities. A base stationmay be an entity that communicates with UEs and relay stations and mayalso be referred to as a node, a Node B, an evolved Node B (eNB), anaccess point, etc. Each base station may provide communication coveragefor a particular geographic area. In 3GPP, the term “cell” can refer toa coverage area of a base station and/or a base station subsystemserving this coverage area, depending on the context in which the termis used. In 3GPP2, the term “sector” or “cell-sector” can refer to acoverage area of a base station and/or a base station subsystem servingthis coverage area. For clarity, 3GPP concept of “cell” is used in thedescription herein.

A base station may provide communication coverage for a macro cell, apico cell, a femto cell, and/or other types of cell. A macro cell maycover a relatively large geographic area (e.g., several kilometers inradius) and may allow unrestricted access by UEs with servicesubscription. A pico cell may cover a relatively small geographic areaand may allow unrestricted access by UEs with service subscription. Afemto cell may cover a relatively small geographic area (e.g., a home)and may allow restricted access by UEs having association with the femtocell (e.g., UEs in a Closed Subscriber Group (CSG)). In the exampleshown in FIG. 1, wireless network 100 includes macro base stations 110 aand 110 b for macro cells, pico base stations 110 c and 110 e for picocells, and a femto/home base station 110 d for a femto cell.

Wireless network 100 may also include relay stations. A relay stationmay be an entity that receives a transmission of data from an upstreamentity (e.g., a base station or a UE) and sends a transmission of thedata to a downstream entity (e.g., a UE or a base station). A relaystation may also be a UE that relays transmissions for other UEs. Arelay station may also be referred to as a node, a station, a relay, arelay base station, etc.

Wireless network 100 may be a heterogeneous network that includes basestations of different types, e.g., macro base stations, pico basestations, femto base stations, relay stations, etc. These differenttypes of base stations may have different transmit power levels,different coverage areas, and different impact on interference inwireless network 100. For example, macro base stations may have a hightransmit power level (e.g., 20 Watts or 43 dBm), pico base stations andrelay stations may have a lower transmit power level (e.g., 2 Watts or33 dBm), and femto base stations may have a low transmit power level(e.g., 0.2 Watts or 23 dBm). Different types of base stations may belongin different power classes having different maximum transmit powerlevels.

A network controller 130 may couple to a set of base stations and mayprovide coordination and control for these base stations. Networkcontroller 130 may communicate with base stations 110 via a backhaul.Base stations 110 may also communicate with one another via thebackhaul.

UEs 120 may be dispersed throughout wireless network 100, and each UEmay be stationary or mobile. A UE may also be referred to as a station,a terminal, a mobile station, a subscriber unit, etc. A UE may be acellular phone, a personal digital assistant (PDA), a wireless modem, awireless communication device, a handheld device, a laptop computer, acordless phone, a wireless local loop (WLL) station, etc. A UE may beable to communicate with base stations, relay stations, other UEs, etc.

A UE may be located within the coverage of one or more base stations. Inone design, a single base station may be selected to serve the UE onboth the downlink and uplink. In another design, one base station may beselected to serve the UE on each of the downlink and uplink. For bothdesigns, a serving base station may be selected based on a metric suchas maximum geometry, minimum pathloss, maximum energy/interferenceefficiency, maximum user throughput, etc. Geometry relates to receivedsignal quality, which may be quantified by a carrier-over-thermal (CoT),a signal-to-noise ratio (SNR), a signal-to-noise-and-interference ratio(SINR), a carrier-to-interference ratio (C/I), etc. Maximizingenergy/interference efficiency may entail (i) minimizing a requiredtransmit energy per bit or (ii) minimizing a received interferenceenergy per unit of received useful signal energy. Part (ii) maycorrespond to maximizing the ratio of channel gain for an intended nodeto a sum of channel gains for all interfered nodes. Part (ii) may beequivalent to minimizing pathloss for the uplink but may be differentfor the downlink. Maximizing user throughput may take into accountvarious factors such as the loading of a base station (e.g., the numberof UEs currently served by the base station), the amount of resourcesallocated to the base station, the available backhaul capacity of thebase station, etc.

The wireless network may support a set of resources for the uplink. Theavailable resources may be defined based on time, or frequency, or bothtime and frequency, or some other criteria. For example, the availableresources may correspond to different frequency subbands, or differenttime interlaces, or different time-frequency blocks, etc. A timeinterlace may include evenly spaced time slots, e.g., every S-th timeslot, where S may be any integer value. The available resources may bedefined for the entire wireless network.

The available resources for the uplink may be used by base stations inthe wireless network in various manners. In one scheme, each basestation may use all of the available resources. This scheme may resultin some UEs achieving poor performance. For example, femto base station110 d in FIG. 1 may be located within the vicinity of macro basestations 110 a and 110 b, and a transmission from UE 120 e to femto basestation 110 d may observe high interference due to a transmission fromUE 120 f to macro base station 110 b. In another scheme, the availableresources may be allocated to base stations based on a fixed resourcepartitioning. Each base station may then use its allocated resources forits UEs. This scheme may enable each base station to achieve goodperformance on its allocated resources. However, some base stations maybe allocated more resources than required whereas some other basestations may require more resources than allocated, which may lead tosuboptimal performance for the wireless network.

In an aspect, adaptive resource partitioning may be performed todynamically allocate the available resources for the uplink to nodes sothat good performance can be achieved. Resource partitioning may also bereferred to as resource allocation, resource coordination, etc. Foradaptive resource partitioning for the uplink, the available resourcesmay be allocated to nodes by assigning each node with a list of targetIoT levels for the available resources, as described below. Adaptiveresource partitioning may be performed in a manner to maximize a utilityfunction. Adaptive resource partitioning is in contrast to fixed orstatic resource partitioning, which allocates a fixed subset of theavailable resources to each node.

FIG. 1 shows uplink transmission from each UE to its serving node. Asshown in FIG. 1, each node may receive desired transmissions from UEsserved by that node as well as interfering transmissions from UEscommunicating with neighbor nodes. An uplink transmission from a givenUE may thus cause interference at each neighbor node.

A target IoT level for a given node p on a given resource r may indicatean amount of interference expected by node p on resource r. Neighbornodes may control uplink transmissions from their UEs on resource r suchthat the total interference caused by these UEs is maintained at orbelow the target IoT level on resource r at node p. Correspondingly,node p may control uplink transmissions from its UEs on each resourcesuch that the interference caused by the UEs is maintained at or belowthe target IoT level for each neighbor node on that resource.

A lower target IoT level for node p on a given resource may mean thatthe resource will be used less by neighbor nodes, since the amount ofinterference expected by node p will be lower. Hence, uplinktransmissions from other UEs communicating with the neighbor nodes willbe sent at lower transmit power levels in order to meet the lower targetIoT level for node p. A target IoT level on the uplink may be equivalentto a transmit PSD level on the downlink.

In one design, adaptive resource partitioning may be performed in acentralized manner. In this design, a designated entity may receivepertinent information for UEs and nodes, compute metrics for resourcepartitioning, select the best resource partitioning based on thecomputed metrics, and provide a list of target IoT levels correspondingto the selected resource partitioning for each node. In another design,adaptive resource partitioning may be performed in a distributed mannerby a set of nodes. In this design, each node may compute certain metricsand may exchange metrics with neighbor nodes. The metric computation andexchange may be performed for one or more rounds. Each node may thendetermine/select the resource partitioning that can provide the bestperformance.

Table 1 lists a set of components that may be used for adaptive resourcepartitioning for the uplink.

TABLE 1 Component Description Active Set A set of nodes maintained for agiven UE t and denoted as AS(t). Neighbor Set A set of nodes maintainedfor a given node p and denoted as NS(p). Resources Time and/or frequencyresources that may be allocated to nodes. Target IoT A list of targetIoT levels for a node on available Levels resources. Utility A functionused to quantify the performance of different Function possible resourcepartitioning.

In one design, an active set may be maintained for each UE and may bedetermined based on pilot measurements made by the UE and/or pilotmeasurements made by nodes. An active set for a given UE t may includenodes that (i) have non-negligible contribution to signal orinterference observed by UE t on the downlink and/or (ii) receivenon-negligible signal or interference from UE t on the uplink. In onedesign, a node may be included in the active set of UE t if the CoT ofthis node is greater than a threshold of CoT_(min). A node may also beincluded in the active set based on received signal strength, pathloss,and/or other criteria. The active set may be limited in order to reducecomputation complexity for adaptive resource partitioning. In onedesign, the active set may be limited to N nodes and may include up to Nstrongest nodes with CoT exceeding CoT_(min), where N may be anysuitable value.

In one design, a neighbor set may be maintained for each node and mayinclude nodes that participate in adaptive resource partitioning. Theneighbor set for each node may be determined based on active sets ofUEs. In one design, a neighbor set for a given node p may include (i)nodes that are in the active sets of UEs served by node p and (ii) nodesserving UEs that have node p in their active sets. The neighbor set maythus include node p and its neighbor nodes.

FIG. 2 shows exemplary active sets for UEs and exemplary neighbor setsfor nodes in FIG. 1. The active set for each UE is shown withinparenthesis next to the UE in FIG. 2, with the serving node/base stationbeing underlined. For example, the active set for UE1 is {M1, M2}, whichmeans that the active set includes serving node M1 and neighbor node M2.The neighbor set for each node is shown within brackets next to the nodein FIG. 2. For example, the neighbor set for node M1 is [M2, P1, P2, F1]and includes macro node M2, pico nodes P1 and P2, and femto node F1.

In one design, a list of target IoT levels for a given node p may bemapped to a list of target interference levels for node p for eachneighbor node. A target interference level may also be referred to as anopen loop projection target. The total noise and interference observedby node p on a given resource r may be equal to thermal noise plus totalinterference from UEs communicating with the neighbor nodes. The targetIoT level for node p on resource r may be mapped to an expected totalinterference observed by node p from UEs served by the neighbor nodes.In one design, the expected total interference may be divided equallyamong all neighbor nodes. The target interference levels may then bedefined as follows:

$\begin{matrix}{{{I_{target}\left( {p,r} \right)} = \frac{{{IoT}_{target}\left( {p,r} \right)} - 1}{N_{neighbor}\left( {p,r} \right)}},} & {{Eq}\mspace{14mu}(1)}\end{matrix}$where IoT target (p, r) is a target IoT level for node p on resource r,

-   -   I_(target)(P r) is a target interference level (or open loop        projection target) for node p on resource r for each neighbor        node, and    -   N_(neighbor)(p, r) is the number of neighbor nodes that use        resource r.

In equation (1), the numerator gives the total interference observed bynode p on resource r by subtracting 1 for thermal noise from the targetIoT level. The total interference is given in thermal noise units. Thetotal interference is divided by the number of neighbor nodes usingresource r to obtain the target interference level for node p onresource r for each neighbor node. For example, the target IoT level fornode p on resource r may be 6 decibels (dB) and may correspond to anexpected total noise and interference that is four times thermal noise.The expected total interference on resource r may be three thermal noiseunits. If node p has three neighbor nodes using resource r, then theexpected interference from each neighbor node on resource r may be onethermal noise unit. Each neighbor node may control uplink transmissionsfrom its UEs such that these uplink transmissions will be at or belowthe target interference level for the neighbor node at node p.

Node p may obtain a list of target IoT levels for each neighbor node andmay determine a list of target interference levels for that neighbornode. For each UE served by node p, a set of transmit PSD levels may becomputed for each resource based on the target interference levels forall neighbor nodes in the active set of the UE, as follows:

$\begin{matrix}{{{{PSD}\left( {t,q,r} \right)} = \frac{{I_{target}\left( {q,r} \right)} \cdot N_{0}}{G\left( {q,t} \right)}},} & {{Eq}\mspace{14mu}(2)}\end{matrix}$where I_(target)(q, r) is a target interference level for neighbor nodeq on resource r, which is determined by the target IoT level for basestation q on resource r,

-   -   G(q, t) is a channel gain between UE t and neighbor node q,    -   PSD(t,q,r) is a transmit PSD level for UE t on resource r that        can meet the target interference level for neighbor node q on        resource r, and    -   N₀ is ambient interference and thermal noise observed by node p.

The transmit PSD level for UE t on each resource may be selected asfollows:

$\begin{matrix}{{{{PSD}\left( {t,r} \right)} \leq {\min\limits_{{q \in {{AS}{(t)}}},{q \neq {S{(t)}}}}{{PSD}\left( {t,q,r} \right)}}},} & {{Eq}\mspace{14mu}(3)}\end{matrix}$where S(t) is a serving node for UE t.

In equation (2), the transmit PSD that UE t can use on resource r may becomputed for each neighbor node q based on the channel gain between UE tand neighbor node q and the target interference level for neighbor nodeq on resource r. The channel gains for the neighbor nodes may beobtained based on pilot measurements from UE t. A set of transmit PSDlevels may be obtained for all neighbor nodes in the active set of UE tfor resource r. The smallest transmit PSD level in the set may beselected as the transmit PSD level for UE t on resource r. This wouldthen ensure that the interference caused by UE t on resource r will notexceed the target interference level for any neighbor node on resourcer. Equation (2) is based on open loop projection using channel gains.The target IoT may be achieved with closed loop power control for UEs.

The transmit PSD levels for UE t on all resources may be limited by themaximum transmit power level of UE t, as follows:

$\begin{matrix}{{{\sum\limits_{r}{{{PSD}\left( {t,r} \right)} \cdot {W(r)}}} \leq {P_{\max}(t)}},} & {{Eq}.\mspace{14mu}(4)}\end{matrix}$where W(r) is the bandwidth of resource r, and

-   -   P_(max)(t) is the maximum transmit power level of UE t.

The spectral efficiency of UE t on resource r may be estimated based onan assumption that serving node p will observe the target IoT level onresource r, as follows:

$\begin{matrix}{{{{SE}\left( {t,r} \right)} = {C\left( \frac{{{PSD}\left( {t,r} \right)} \cdot {G\left( {p,t} \right)}}{N_{0} \cdot {{IoT}_{target}\left( {p,r} \right)}} \right)}},} & {{Eq}\mspace{14mu}(5)}\end{matrix}$where G(p,t) is a channel gain between serving node p and UE t,

-   -   SE(t, r) is a spectral efficiency of UE t on resource r, and    -   C( ) denotes a capacity function.

In equation (5), the numerator within the parenthesis denotes thedesired received power for UE t at serving node p. The denominatordenotes the expected total noise and interference at serving node p. Thechannel gain between UE t and serving node p may be obtained based onpilot measurements from UE t. The capacity function may be a constrainedcapacity function, an unconstrained capacity function, or some otherfunction. As shown in equation (5), the spectral efficiency of UE t onresource r is dependent on (i) the transmit PSD for UE t on resource r,which is dependent on the target IoT levels for the neighbor nodes onresource r, and (ii) the target IoT level for node p on resource r. Thetarget IoT levels thus affect both the numerator and denominator ofequation (5).

The rate that UE t can achieve on all resources may be dependent onwhich resources are assigned to UE t. In one design, the rate for UE tmay be estimated by assuming that UE t is assigned a fraction of eachavailable resource. This fraction may be denoted as α(t, r) and may beviewed as the fraction of time during which resource r is assigned to UEt. The rate for UE t may then be computed as follows:

$\begin{matrix}{{{R(t)} = {{\sum\limits_{r}{{\alpha\left( {t,r} \right)} \cdot {{SE}\left( {t,r} \right)} \cdot {W(r)}}} = {\sum\limits_{r}{{\alpha\left( {t,r} \right)} \cdot {R\left( {t,r} \right)}}}}},} & {{Eq}\mspace{14mu}(6)}\end{matrix}$where R(t, r) is the rate for UE t on resource r, and

-   -   R(t) is the rate for UE t on all resources.

A pre-scheduler may perform scheduling forecast and may maximize theutility function over the space of the a(t, r) parameter, as follows:

$\begin{matrix}{{{maximize}\mspace{14mu}{U(p)}},{{{for}\mspace{14mu} 0} \leq {\alpha\left( {t,r} \right)} \leq {1\mspace{14mu}{and}\mspace{14mu}{\sum\limits_{{S{(t)}} = p}{\alpha\left( {t,r} \right)}}} \leq 1},} & {{Eq}\mspace{14mu}(7)}\end{matrix}$where U(p) denotes a utility function for node p.

Utility function U(p) for node p may be defined in various manners. Forexample, utility function U(p) may be equal to the sum of rates of allUEs served by node p, or equal to the minimum of rates of all servedUEs, or equal to the sum of the log of the rates of all served UEs, orequal to some other function of rate, latency, queue size, etc. In onedesign, the utility function for node p may be defined as follows:

$\begin{matrix}\begin{matrix}{{U(p)} = {\sum\limits_{{S{(t)}} = p}{f(t)}}} \\{= {\sum\limits_{{S{(t)}} = p}{f\left( {R(t)} \right)}}} \\{{= {\sum\limits_{{S{(t)}} = p}{f\left( {\sum\limits_{r}{{\alpha\left( {t,r} \right)} \cdot {R\left( {t,r} \right)} \cdot {W(r)}}} \right)}}},}\end{matrix} & {{Eq}\mspace{14mu}(8)}\end{matrix}$where ƒ(t) denotes a metric function for UE t.

Function ƒ(t) for UE t may be defined in various manners. For example,function ƒ(t) may be equal to rate and given as ƒ(t)=R(t), or equal tothe log of rate and given as ƒ(t)=log R(t), or equal to the log of logof rate and given as ƒ(t)=log {log R(t)}, or equal to one over the cubeof rate and given as ƒ(t)=−1/R(t)³, or equal to some other function ofrate, latency, queue size, etc. Function ƒ(t) may also be defined tocapture minimum guaranteed rates of UEs, e.g., by selecting a largeslope before a guaranteed rate.

The rate for UE t may be constrained as follows:R(t)≦R _(max)(t),   Eq (9)where R_(max)(t) is the maximum rate supported by UE t. R_(max)(t) maybe given by a traffic profile or may be estimated based on paststatistics. For a relay station, R_(max)(t) may represent the accesslink capacity of the relay station.

An overall rate R(p) for node p may also be constrained as follows:

$\begin{matrix}{{{R(p)} = {{\sum\limits_{{S{(t)}} = p}{R(t)}} \leq {R_{BH}(p)}}},} & {{Eq}\mspace{14mu}(10)}\end{matrix}$where R_(BH)(p) is a backhaul rate for node p.

In one design, an adaptive algorithm may be used for resourcepartitioning. The algorithm is adaptive in that it can take intoconsideration the current operating scenario, which may be different fordifferent parts of the wireless network and may also change over time.The adaptive algorithm may be performed by each node in a distributedmanner and may attempt to maximize the utility function over a set ofnodes or possibly across the entire wireless network.

FIG. 3 shows a design of a process 300 for performing adaptive resourcepartitioning for the uplink. Process 300 may be performed by each nodein a neighbor set for a distributed design. For clarity, process 300 isdescribed below for node p. Node p may obtain a list of target IoTlevels for available resources for each node in the neighbor set (step312). Node p may obtain the target IoT levels for the neighbor nodes viathe backhaul or through other means.

Node p may determine a list of possible actions related to resourcepartitioning that can be performed by node p and/or neighbor nodes (step314). Each possible action may correspond to a specific list of targetIoT levels for node p as well as a specific list of target IoT levelsfor each neighbor node. For example, a possible action may entail node pchanging its target IoT level on a particular resource and/or a neighbornode changing its target IoT level on the resource. The list of possibleactions may include (i) standard actions that may be evaluatedperiodically without any explicit request and/or (ii) on-demand actionsthat may be evaluated in response to requests from neighbor nodes. Somepossible actions are described below. The list of possible actions maybe denoted as A.

Node p may compute local metrics for different possible actions based onthe lists of target IoT levels for all nodes in the neighbor set (block316). For each possible action, node p may compute a target interferencelevel for each neighbor node on each resource based on the target IoTlevel for the neighbor node on the resource, e.g., as shown in equation(1). Node p may compute transmit PSD levels for its UEs on the availableresources based on the target interference levels and the channel gainsbetween the UEs and the neighbor nodes, e.g., as shown in equation (2).Node p may compute rates for the UEs based on the transmit PSD levelsfor the UEs and the target IoT levels for node p on the availableresources, e.g., as shown in equations (5) and (6). Node p may thencompute a local metric for the possible action based on a utilityfunction of the rates for the UEs and/or other parameters, e.g., asshown in equation (8). A local metric may indicate the performance of anode for a given action. For example, a local metric based on a sum rateutility function may indicate the overall rate achieved by node p for aparticular action a and may be computed as follows:

$\begin{matrix}{{{U\left( {p,a} \right)} = {\sum\limits_{{S{(t)}} = p}{R\left( {t,a} \right)}}},} & {{Eq}\mspace{14mu}(11)}\end{matrix}$where R(t, a) is a rate for UE t on all available resources for actiona, and

-   -   U(p,a) is a local metric for node p for action a.

The rate R(t,a) for each UE may be computed as shown in equations (1)through (6), where IoT_(target)(q,r) in equation (2) andIoT_(target)(p,r) in equation (5) may be dependent on the lists oftarget IoT levels for nodes q and p, respectively, for possible actiona. In the design shown in equation (11), the rate for each UE on allavailable resources may first be determined, and the rates for all UEsserved by node p may then be summed to obtain the local metric for nodep. In another design, the rate for each UE on each available resourcemay first be determined, the rates for all UEs on each availableresource may then be computed, and the rates for all available resourcesmay be summed to obtain the local metric for node p. The local metricfor node p for each possible action may also be computed in othermanners and may be dependent on the utility function.

The local metrics for different possible actions may be used by node pas well as the neighbor nodes to compute overall metrics for differentpossible actions. Node p may send its computed local metrics U(p,a), fora ε A, to the neighbor nodes (block 318). Node p may also receive localmetrics U(q,a), for a ε A, from each neighbor node q in the neighbor set(block 320). Node p may compute overall metrics for different possibleactions based on its computed local metrics and the received localmetrics (block 322). For example, an overall metric based on the sumrate utility function may be computed for each possible action a, asfollows:

$\begin{matrix}{{{V(a)} = {{U\left( {p,a} \right)} + {\sum\limits_{q \in {{{NS}{(p)}}\backslash{\{ p\}}}}{U\left( {q,a} \right)}}}},} & {{Eq}\mspace{14mu}(12)}\end{matrix}$where V(a) is an overall metric for possible action a.

The summation in equation (12) is over all nodes in the neighbor setexcept for node p. The computation of the overall metric may be modifiedaccordingly for other types of utility function. For example, asummation for the overall metric may be replaced with a minimumoperation for a utility function that minimizes a particular parameter.

After completing the metric computation, node p may select the actionwith the best overall metric (block 324). Each neighbor node maysimilarly compute overall metrics for different possible actions and mayalso select the action with the best overall metric. Node p and theneighbor nodes should select the same action if they operate on the sameset of local metrics. Each node may then operate based on the selectedaction, without having to communicate with one another regarding theselected action. However, node p and its neighbor nodes may operate ondifferent local metrics and may obtain different best overall metrics.This may be the case, for example, if node p and its neighbor nodes havedifferent neighbor sets. In this case, node p may negotiate with theneighbor nodes to determine which action to take. This may entailexchanging overall metrics for some promising actions between the nodesand selecting the action that can provide good performance for as manynodes as possible.

Regardless of how the best action is selected, the selected action isassociated with a list of target IoT levels for node p and a list oftarget IoT levels for each neighbor node. Node p may schedule its UEsfor transmission on the available resources based on its target IoTlevels as well as the target IoT levels for the neighbor nodes (block326). For example, node p may assign the available resources to its UEs.Node p may determine the transmit PSD level for each UE on each assignedresource based on the target IoT levels for all neighbor nodes on thatresource. Node p may ensure that the transmit PSD level for each UE oneach resource will not cause excessive interference to each neighbornode in the active set of that UE, e.g., as shown in equations (2) and(3). Node p may also determine a rate for each UE based on the transmitPSD level on each assigned resource and the target IoT levels for nodep, e.g., as shown in equations (5) and (6), where α(t,r)=1 for eachassigned resource and α(t, r)=0 for each unassigned resource. Node p mayschedule each UE for data transmission at the rate determined for thatUE.

In one design, a set of target IoT levels may be defined. A target IoTlevel for a node on a resource may be equal to one of the target IoTlevels in the set. The target IoT levels for each node may thus bequantized based on the set of target IoT levels. This may reducecomputation complexity for resource partitioning. In one design, a setof three target IoT levels may be used. Fewer or more target IoT levelsmay also be supported. In one design, the same set of target IoT levelsmay be used for all nodes. In another design, different sets of targetIoT levels may be used for different nodes or different types of nodes.For example, one set of target IoT levels may be used for macronodes/base stations and another set of target IoT levels may be used forpico and femto nodes/base stations.

There may be a large number of possible actions to evaluate for anexhaustive search to find the best action. The number of possibleactions to evaluate may be reduced in various manners. In one design,each available resource may be treated independently, and a given actionmay change the target IoT level of only one resource. In another design,the number of nodes that can adjust their target IoT levels on a givenresource for a given action may be limited. In yet another design, thetarget IoT for a given node on a given resource may be either increasedor decreased by one level at a time. The number of possible actions mayalso be reduced via other simplifications.

In one design, a list of possible actions that may lead to good overallmetrics may be evaluated. Possible actions that are unlikely to providegood overall metrics may be skipped in order to reduce computationcomplexity. For example, having both node p and a neighbor node increasetheir target IoT levels on the same resource will likely result in extrainterference on the resource, which may degrade performance for bothnodes. This possible action may thus be skipped.

Table 2 lists different types of actions that may be evaluated foradaptive resource partitioning for the uplink, in accordance with onedesign.

TABLE 2 Action Types Action Type Description p-C-r Node p claimsresource r and decreases its target IoT by one level on resource r.p-B-r Node p blanks resource r and increases its target IoT by one levelon resource r. p-R-r-Q Node p requests resource r from one or moreneighbor nodes in set Q and asks the neighbor node(s) in set Q toincrease their target IoT by one level on resource r. p-G-r-Q Node pgrants resource r to one or more neighbor nodes in set Q and tells theneighbor node(s) in set Q to decrease their target IoT by one level onresource r. p-CR-r-Q Node p claims and requests resource r from one ormore neighbor nodes in set Q and (i) decreases its target IoT by onelevel on resource r and (ii) asks the neighbor node(s) in set Q toincrease their target IoT by one level on resource r. p-BG-r-Q Node pblanks and grants resource r to one or more neighbor nodes in set Q and(i) increases its target IoT by one level on resource r and (ii) tellsthe neighbor node(s) in set Q to decrease their target IoT by one levelon resource r.

Each action type in Table 2 may be associated with a set of possibleactions of that type. For each action type involving only node p, Kpossible actions may be evaluated for K available resources. For eachaction type involving both node p and one or more neighbor nodes in setQ, multiple possible actions may be evaluated for each availableresource, with the number of possible actions being dependent on thesize of the neighbor set, the size of set Q, etc. Node p may compute alocal metric for each possible action of each action type. The localmetrics for all possible actions may be used to compute overall metricsfor different possible actions.

Table 2 lists some types of actions that may be evaluated for adaptiveresource partitioning for the uplink. Fewer, more and/or differentaction types may also be evaluated.

In one design, adaptive resource partitioning may be performed for allavailable resources for the uplink. In another design, adaptive resourcepartitioning may be performed for a subset of the available resources.For example, macro nodes may be allocated a first subset of theavailable resources and pico nodes may be allocated a second subset ofthe available resources based on fixed resource partitioning. Theremaining available resources may be dynamically allocated to the macronodes or the pico nodes based on adaptive resource partitioning.

FIG. 4 shows a design of a process 400 for supporting communication.Process 400 may be performed by a serving node (as described below) orby some other entity. The serving node may be a base station, a relaystation, or some other entity. The serving node may identify at leastone neighbor node, e.g., based on active sets of UEs communicating withthe serving node and active sets of UEs communicating with the at leastone neighbor node (block 412). The serving node may obtain at least onelist of target IoT levels for the at least one neighbor node onavailable resources, one list of target IoT levels for each neighbornode (block 414). The serving node may also obtain a list of target IoTlevels for itself on the available resources (block 416). Each list oftarget IoT levels may include K configurable target IoT levels on Kavailable resources for an associated node, one target IoT level foreach available resource. The serving node may schedule at least one UEfor transmission on the available resources based on the at least onelist of target IoT levels for the at least one neighbor node and thelist of target IoT levels for the serving node (block 418).

In another design, the serving node may obtain a list of targetinterference levels (instead of a list of target IoT levels) for eachneighbor node. The target IoT levels obtained by the serving node inblocks 414 and 416 may thus generically refer to interferenceinformation in any suitable form.

FIG. 5 shows a design of an apparatus 500 for supporting communication.Apparatus 500 includes a module 512 to identify at least one neighbornode of a serving node for at least one UE, a module 514 to obtain atleast one list of target IoT levels for the at least one neighbor nodeon available resources, a module 516 to obtain a list of target IoTlevels for the serving node on the available resources, and a module 518to schedule the at least one UE for transmission on the availableresources to the serving node based on the at least one list of targetIoT levels for the at least one neighbor node and the list of target IoTlevels for the serving node.

FIG. 6 shows a design of a process 600 for performing adaptive resourcepartitioning for the uplink. Process 600 may be used for blocks 414 and416 in FIG. 4 and may be performed by a serving node (as describedbelow) or some other entity. The serving node may compute local metricsfor a plurality of possible actions related to allocation of availableresources to the serving node and at least one neighbor node in aneighbor set (block 612). Each possible action may be associated with aplurality of lists of target IoT levels for the serving and neighbornodes. The serving node may send the computed local metrics to the atleast one neighbor node to enable the neighbor node(s) to computeoverall metrics for the plurality of possible actions (block 614). Theserving node may receive local metrics for the plurality of possibleactions from the at least one neighbor node (block 616). A local metricfor a possible action may be indicative of the performance achieved by anode for the possible action. An overall metric for a possible actionmay be indicative of the overall performance achieved by the serving andneighbor nodes for the possible action.

The serving node may determine overall metrics for the plurality ofpossible actions based on the computed local metrics and the receivedlocal metrics for the plurality of possible actions, e.g., as shown inequation (12) (block 618). The serving node may select one of theplurality of possible actions based on the overall metrics, e.g., selectthe possible action with the best overall metric (block 620). Theserving node may determine a list of target IoT levels for itself and atleast one list of target IoT levels for the at least one neighbor nodefrom the plurality of lists of target IoT levels associated with theselected action (block 622).

In one design of block 612, the serving node may compute a local metricfor each possible action by first determining at least one rate for atleast one UE based on the plurality of lists of target IoT levelsassociated with the possible action. The serving node may then determinethe local metric for the possible action based on the at least one rate,e.g., as shown in equation (11). The local metrics may be computed basedon a function of rate, or latency, or queue size, or a combinationthereof Various simplifications may be made to reduce computationcomplexity for adaptive resource partitioning. For example, eachpossible action may affect only one of the available resources, or maychange the target IoT by at most one level for each node, or may be ofone of a set of action types, etc.

FIG. 6 shows a distributed design in which a set of nodes may eachcompute and exchange local metrics and possibly overall metrics fordifferent possible actions. For a centralized design, a designatedentity may compute local metrics and overall metrics for differentpossible actions, select the best possible action, determine the listsof target IoT levels associated with the selected action for all nodes,and provide a list of target IoT levels to each node.

FIG. 7 shows a design of a process 700 for scheduling UEs. Process 700may be used for block 418 in FIG. 4 and may be performed by a servingnode (as described below) or some other entity. The serving node mayobtain target interference levels for at least one neighbor node onavailable resources, which may be determined based on at least one listof target IoT levels for the at least one neighbor node (block 712). Theserving node may compute the target interference levels for the neighbornodes or may receive the target interference levels from the neighbornodes. The target interference level for each neighbor node on eachavailable resource may be determined based on (i) a target IoT level forthe neighbor node on the available resource and (ii) the number of othernodes using the available resource, e.g., as shown in equation (1).

The serving node may determine transmit PSD levels for at least one UEon the available resources based on the target interference levels forthe at least one neighbor node on the available resources (block 714).For each UE, the serving node may determine at least one transmit PSDlevel for the UE on each available resource based on (i) at least onetarget interference level for the at least one neighbor node on theavailable resource and (ii) at least one channel gain between the UE andthe at least one neighbor node, e.g., as shown in equation (2). Theserving node may select the smallest transmit PSD level among the atleast one PSD level for the UE on each available resource, e.g., asshown in equation (3).

The serving node may determine rates R(t, r) for the at least one UEbased on (i) the transmit PSD levels for the at least one UE on theavailable resources and (ii) a list of target IoT levels for the servingnode on the available resources, e.g., as shown in equations (5) and (6)(block 716). The serving node may assign the available resources to theat least one UE, and each UE may be assigned at least one resource(block 718). The resource assignment may be based on the rates achievedby the at least one UE on the available resources. The serving node maydetermine the rate R(t) for each UE based on the at least one resourceassigned to the UE and the rate R(t, r) achieved by the UE on eachassigned resource, e.g., as shown in equation (6) (block 720).

FIG. 8 shows a design of a process 800 for communicating in a wirelessnetwork. Process 800 may be performed by a UE (as described below) or bysome other entity. The UE may make pilot measurements for nodesdetectable by the UE (block 812). The pilot measurements may be used todetermine an active set for the UE, to compute metrics for adaptiveresource partitioning, and/or for other purposes.

The UE may receive an assignment of at least one resource from a servingnode (block 814). The UE may obtain at least one transmit PSD level foritself on the at least one resource (block 816). The at least onetransmit PSD level may be determined based on configurable target IoTlevels for at least one neighbor node on the at least one resource. Thetransmit PSD level may be computed via open loop projection (e.g., bythe serving node) and/or may be adjusted via closed loop power control.The UE may obtain a rate R(t) determined based on (i) the at least onetransmit PSD level for the UE on the at least one resource and (ii) atleast one target IoT level for the serving node on the at least oneresource (block 818). The rate may be computed by the serving node andsent to the UE. The UE may send a transmission on the at least oneresource based on the at least one transmit PSD level and at theobtained rate (block 820).

FIG. 9 shows a design of an apparatus 900 for communicating in awireless network. Apparatus 900 includes a module 912 to make pilotmeasurements for nodes detectable by a UE, a module 914 to receive anassignment of at least one resource from a serving node at the UE, amodule 916 to obtain at least one transmit PSD level for the UE on theat least one resource, the at least one transmit PSD level beingdetermined based on configurable target IoT levels for at least oneneighbor node on the at least one resource, a module 918 to obtain arate determined based on the at least one transmit PSD level for the UEon the at least one resource and at least one target IoT level for theserving node on the at least one resource, and a module 920 to send atransmission on the at least one resource based on the at least onetransmit PSD level and at the obtained rate.

The modules in FIGS. 5 and 9 may comprise processors, electronicdevices, hardware devices, electronic components, logical circuits,memories, software codes, firmware codes, etc., or any combinationthereof.

FIG. 10 shows a block diagram of a design of a base station/node 110 anda UE 120, which may be one of the base stations and one of the UEs inFIG. 1. Base station 110 may be equipped with T antennas 1034 a through1034 t, and UE 120 may be equipped with R antennas 1052 a through 1052r, where in general T≧1 and R≧1.

At base station 110, a transmit processor 1020 may receive data from adata source 1012 for one or more UEs and control information from acontroller/processor 1040. Processor 1020 may process (e.g., encode,interleave, and modulate) the data and control information to obtaindata symbols and control symbols, respectively. Processor 1020 may alsogenerate pilot symbols for pilot or reference signal. A transmit (TX)multiple-input multiple-output (MIMO) processor 1030 may perform spatialprocessing (e.g., precoding) on the data symbols, the control symbols,and/or the pilot symbols, if applicable, and may provide T output symbolstreams to T modulators (MODs) 1032 a through 1032 t. Each modulator1032 may process a respective output symbol stream (e.g., for OFDM,etc.) to obtain an output sample stream. Each modulator 1032 may furtherprocess (e.g., convert to analog, amplify, filter, and upconvert) theoutput sample stream to obtain a downlink signal. T downlink signalsfrom modulators 1032 a through 1032 t may be transmitted via T antennas1034 a through 1034 t, respectively.

At UE 120, antennas 1052 a through 1052 r may receive the downlinksignals from base station 110 and may provide received signals todemodulators (DEMODs) 1054 a through 1054 r, respectively. Eachdemodulator 1054 may condition (e.g., filter, amplify, downconvert, anddigitize) its received signal to obtain input samples. Each demodulator1054 may further process the input samples (e.g., for OFDM, etc.) toobtain received symbols. A MIMO detector 1056 may obtain receivedsymbols from all R demodulators 1054 a through 1054 r, perform MIMOdetection on the received symbols if applicable, and provide detectedsymbols. A receive processor 1058 may process (e.g., demodulate,deinterleave, and decode) the detected symbols, provide decoded data forUE 120 to a data sink 1060, and provide decoded control information to acontroller/processor 1080.

On the uplink, at UE 120, a transmit processor 1064 may receive andprocess data from a data source 1062 and control information fromcontroller/processor 1080. Processor 1064 may also generate pilotsymbols for pilot or reference signal. The symbols from transmitprocessor 1064 may be precoded by a TX MIMO processor 1066 ifapplicable, further processed by modulators 1054 a through 1054 r (e.g.,for SC-FDM, OFDM, etc.), and transmitted to base station 110. At basestation 110, the uplink signals from UE 120 may be received by antennas1034, processed by demodulators 1032, detected by a MIMO detector 1036if applicable, and further processed by a receive processor 1038 toobtain decoded data and control information sent by UE 120. Processor1038 may provide the decoded data to a data sink 1039 and the decodedcontrol information to controller/processor 1040.

Controllers/processors 1040 and 1080 may direct the operation at basestation 110 and UE 120, respectively. A channel processor 1084 may makepilot measurements, which may be used to determine an active set for UE120 and to compute channel gains, transmit PSD levels, rates, metrics,etc. Processor 1040 and/or other processors and modules at base station110 may perform or direct process 300 in FIG. 3, process 400 in FIG. 4,process 600 in FIG. 6, process 700 in FIG. 7, and/or other processes forthe techniques described herein. Processor 1080 and/or other processorsand modules at UE 120 may perform or direct process 800 in FIG. 8 and/orother processes for the techniques described herein. Memories 1042 and1082 may store data and program codes for base station 110 and UE 120,respectively. A scheduler 1044 may schedule UEs for data transmission onthe downlink and/or uplink.

Those of skill in the art would understand that information and signalsmay be represented using any of a variety of different technologies andtechniques. For example, data, instructions, commands, information,signals, bits, symbols, and chips that may be referenced throughout theabove description may be represented by voltages, currents,electromagnetic waves, magnetic fields or particles, optical fields orparticles, or any combination thereof.

Those of skill would further appreciate that the various illustrativelogical blocks, modules, circuits, and algorithm steps described inconnection with the disclosure herein may be implemented as electronichardware, computer software, or combinations of both. To clearlyillustrate this interchangeability of hardware and software, variousillustrative components, blocks, modules, circuits, and steps have beendescribed above generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure.

The various illustrative logical blocks, modules, and circuits describedin connection with the disclosure herein may be implemented or performedwith a general-purpose processor, a digital signal processor (DSP), anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA) or other programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Ageneral-purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with thedisclosure herein may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in RAM memory, flash memory, ROM memory,EPROM memory, EEPROM memory, registers, hard disk, a removable disk, aCD-ROM, or any other form of storage medium known in the art. Anexemplary storage medium is coupled to the processor such that theprocessor can read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor. The processor and the storage medium may reside in anASIC. The ASIC may reside in a user terminal In the alternative, theprocessor and the storage medium may reside as discrete components in auser terminal

In one or more exemplary designs, the functions described may beimplemented in hardware, software, firmware, or any combination thereofIf implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. Computer-readable media includes both computerstorage media and communication media including any medium thatfacilitates transfer of a computer program from one place to another. Astorage media may be any available media that can be accessed by ageneral purpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code means in the form of instructions or datastructures and that can be accessed by a general-purpose orspecial-purpose computer, or a general-purpose or special-purposeprocessor. Also, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, includes compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk and blu-ray discwhere disks usually reproduce data magnetically, while discs reproducedata optically with lasers. Combinations of the above should also beincluded within the scope of computer-readable media.

The previous description of the disclosure is provided to enable anyperson skilled in the art to make or use the disclosure. Variousmodifications to the disclosure will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other variations without departing from the spirit or scopeof the disclosure. Thus, the disclosure is not intended to be limited tothe examples and designs described herein but is to be accorded thewidest scope consistent with the principles and novel features disclosedherein.

What is claimed is:
 1. A method for wireless communication, comprising:receiving an assignment of at least one resource from a serving node ata user equipment (UE); obtaining at least one transmit power spectraldensity (PSD) level associated with the UE on the at least one resource,wherein the at least one transmit PSD level is determined based onconfigurable target interference levels associated with at least oneneighbor node on the at least one resource; obtaining a rate wherein therate is determined based on the at least one transmit PSD level and atleast one target interference level associated with the serving node onthe at least one resource; and sending a transmission at the obtainedrate on the at least one resource.
 2. The method of claim 1, furthercomprising: making pilot measurements for nodes detectable by the UE,wherein the pilot measurements are used to determine an active set forthe UE.
 3. The method of claim 2, wherein the pilot measurements areused to compute metrics for resource partitioning to allocate availableresources to the serving node and the at least one neighbor node.
 4. Anapparatus for wireless communication, comprising: means for receiving anassignment of at least one resource from a serving node at a userequipment (UE); means for obtaining at least one transmit power spectraldensity (PSD) level associated with the UE on the at least one resource,wherein the at least one transmit PSD level is determined based onconfigurable target levels associated with at least one neighbor node onthe at least one resource; means for obtaining a rate, wherein the rateis determined based on the at least one transmit PSD level and at leastone target interference level associated with the serving node on the atleast one resource; and means for sending a transmission at the obtainedrate on the at least one resource.
 5. The apparatus of claim 4, furthercomprising: means for making pilot measurements for nodes detectable bythe UE, wherein the pilot measurements are used to determine an activeset for the UE, or to compute metrics for resource partitioning toallocate available resources to the serving node and the at least oneneighbor node, or both.
 6. An apparatus for wireless communication,comprising: a memory; at least one processor coupled to the memory, andconfigured to: receive an assignment of at least one resource from aserving node at a user equipment (UE); obtain at least one transmitpower spectral density (PSD) level associated with the UE on the atleast one resource, wherein the at least one transmit PSD level isdetermined based on configurable target interference levels associatedwith at least one neighbor node on the at least one resource; obtain arate, wherein the rate is determined based on the at least one transmitPSD level and at least one target interference level associated with theserving node on the at least one resource; and send a transmission atthe obtained rate on the at least one resource.
 7. A computer programproduct stored on a non-transitory computer-readable medium, andcomprising code for causing at least one processor to: receive anassignment of at least one resource from a serving node at a userequipment (UE), obtain at least one transmit power spectral density(PSD) level associated with the UE on the at least one resource, whereinthe at least one transmit PSD level is determined based on configurabletarget interference levels associated with at least one neighbor node onthe at least one resource, obtain a rate, wherein the rate is determinedbased on the at least one transmit PSD level and at least one targetinterference level for the serving node on the at least one resource;and send a transmission at the obtained rate on the at least oneresource.